Knowledge partitioning in forecasting

Lee-Xieng Yang, National Chengchi University

Tzu-Hsi Lee, National Chengchi University

Abstract

In this study, we would like to examine whether the learned
forecasting function can be separated for use by context. The participants were
asked to learn to forecast the position of a target, defined as a sine function
of trial number. A context cue was paired with the moving of the target
systematically and randomly in two conditions. The learning performance was quite
good in both conditions. In the transfer phases, in the systematic-context
condition, some participants learned to rely on context to direct their
prediction (i.e., knowledge partitioning), whereas some others and those in the
randomized-context condition learned to rely on the concept about the function
for forecasting. However, contrary to the precedent knowledge partitioning
studies, the variety of using context or not was found within participants across
transfer phases. The modeling results favored the associative account over the
rule account on accommodating the training and transfer response patterns.